Lecture 14 | Deep Reinforcement Learning Lecture 14 | Deep Reinforcement Learning begin-post-stats Stanford University School of Engineering • 62K views end-post-stats begin-duration 1:04:01 end-duration Math topics: _Optimal_decisions_##Optimal decisions##_Markov_decision_process_##Markov decision process##_Bellman_equation_##Bellman equation##_Loss_function_##Loss function##_Dimensionless_numbers_##Dimensionless numbers##_Correlation_and_dependence_##Correlation and dependence##_Slope_##Slope##_Probability_##Probability##_Theory_of_probability_distributions_##Theory of probability distributions##_Expected_value_##Expected value##_Q-function_##Q-function##_Variance_##Variance Other topics: _Machine_learning_##Machine learning##_Deep_learning_##Deep learning##_Q-learning_##Q-learning##_Reinforcement_learning_##Reinforcement learning##_Value_(ethics)_##Value (ethics)##_Good_and_evil_##Good and evil##_Value_theory_##Value theory##_Value_(ethics)_##Value (ethics) video-id: lvoHnicueoE channel_Stanford_University_School_of_Engineering_ So What Learning Dimensionless numbers , Machine learning , Optimal decisions , Theory of probability distributions , Value (ethics) Saturday, January 20, 2018 Share Share